title: MMAD: microarray microdissection with analysis of differences is a computational tool for deconvoluting cell type-specific contributions from tissue samples.
abstract: BACKGROUND
  One of the significant obstacles in the development of clinically relevant microarray-derived biomarkers and classifiers is tissue heterogeneity. Physical cell separation techniques, such as cell sorting and laser-capture microdissection, can enrich samples for cell types of interest, but are costly, labor intensive and can limit investigation of important interactions between different cell types.
  
  
  RESULTS
  We developed a new computational approach, called microarray microdissection with analysis of differences (MMAD), which performs microdissection in silico. Notably, MMAD (i) allows for simultaneous estimation of cell fractions and gene expression profiles of contributing cell types, (ii) adjusts for microarray normalization bias, (iii) uses the corrected Akaike information criterion during model optimization to minimize overfitting and (iv) provides mechanisms for comparing gene expression and cell fractions between samples in different classes. Computational microdissection of simulated and experimental tissue mixture datasets showed tight correlations between predicted and measured gene expression of pure tissues as well as tight correlations between reported and estimated cell fraction for each of the individual cell types. In simulation studies, MMAD showed superior ability to detect differentially expressed genes in mixed tissue samples when compared with standard metrics, including both significance analysis of microarrays and cell type-specific significance analysis of microarrays.
  
  
  CONCLUSIONS
  We have developed a new computational tool called MMAD, which is capable of performing robust tissue microdissection in silico, and which can improve the detection of differentially expressed genes. MMAD software as implemented in MATLAB is publically available for download at http://sourceforge.net/projects/mmad/.
authors.affiliation: Division of Medical Oncology, Department of Internal Medicine, Department of Biomedical Informatics and Comprehensive Cancer Center, Biomedical Informatics Shared Resource, The Ohio State University, Columbus OH 43210, USA.
authors.forename: David A
authors.initials: DA
authors.lastname: Liebner
authors.forename: Kun
authors.initials: K
authors.lastname: Huang
authors.forename: Jeffrey D
authors.initials: JD
authors.lastname: Parvin
mesh_terms: D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D042282:Microdissection / Q000379:methods; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D051381:Rats; D012984:Software; D046888:Tissue Array Analysis / Q000379:methods*